# Wan-2.2 Text-to-Video A14B with LoRAs API

> A14B text-to-video med LoRA-støtte og valgfri baklengs avspilling

Lager en video fra en tekstbasert `prompt` og bruker LoRA-vekter angitt i `loras`. `reverse_video` spiller klippet baklengs. `num_frames` går fra 17 til 161, `resolution` er 480p, 580p eller 720p, og størrelsesforholdet er 16:9, 9:16 eller 1:1. Film- eller rife-interpolering og to styringsskalaer er tilgjengelige.

- Model id: `wan/v2.2-a14b/text-to-video/lora`
- Made by: Alibaba
- Category: Text to Video
- Output: video
- Estimated price per run: from $0.507 / run (cheapest run $0.507 (507 API credits))
- Run: `POST https://api.doitong.ru/v1/run/wan/v2.2-a14b/text-to-video/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/wan/v2.2-a14b/text-to-video/lora`
- Docs page: https://doitong.com/no/developers/models/wan/v2.2-a14b/text-to-video/lora

## Pricing

Price: **from $0.507 / run** (the lowest tier; the quote prices your exact input). Estimated from the typical cost of one run. The quote is exactly what the run charges.

Price rules (the first rule that matches the input wins; a parameter you leave out counts as its default):

- When `num_frames` > 81 and `resolution` = 480p: base price × 2
- When `num_frames` > 81 and `resolution` = 580p: base price × 2
- When `num_frames` > 81 and `resolution` = 720p: base price × 2

The full example input below costs $0.507 = 507 API credits (1 request × 507 API credits).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.ru/v1/quote/wan/v2.2-a14b/text-to-video/lora` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | Tekstinstruksjoner som veileder videogenerering. |
| `negative_prompt` |  | string | no | `` |  | Negativ prompt brukt ved generering av en video. |
| `num_frames` |  | integer | no | `81` | 17 – 161 | Antall bilder for generering, med et inklusivt område på 17–161. |
| `frames_per_second` |  | integer \| null | no | `16` | 4 – 60 | Bildefrekvens for generert video, med verdier fra 4 til 60; setting `adjust_fps_for_interpolation` til false bevarer frekvensen uendret. Ved bruk av interpolering og `adjust_fps_for_interpolation` satt til true (standardverdien) multipliseres frekvensen med én pluss antallet interpolerte bilder for å beregne endelig FPS. Eksempel: 16 FPS med 1 interpolert bilde blir 32 FPS. |
| `seed` |  | integer \| null | no |  |  | Seed som gjør genereringen reproduserbar. Hvis den settes til None, velges en seed tilfeldig. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `580p`, `720p` | Oppløsning for utdata-video: 480p, 580p eller 720p. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `16:9`, `9:16`, `1:1` | Sideforhold for utdata-videoen, enten 16:9 eller 9:16. |
| `num_inference_steps` |  | integer | no | `27` | 2 – 40 | Antall samplingsteg; lengre behandlingstid byttes mot bedre kvalitet når antallet økes. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Sikkerhetskontroll av inndata før behandling, som bare autoriserte kontoer kan deaktivere. Forespørsler uten autorisasjon kontrolleres alltid; ellers aktiverer true kontrollen. |
| `enable_output_safety_checker` |  | boolean | no | `false` |  | Styrer sikkerhetskontroll av den genererte videoen; true utfører en kontroll etter generering. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Lar en stor språkmodell berike prompten med detaljer. Når denne utvidelsen aktiveres, forblir betydningen av prompten uendret. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | Akselerasjonsinnstilling, der 'regular' er det anbefalte valget. Økt akselerasjon reduserer kvaliteten for å korte ned genereringstiden. |
| `guidance_scale` |  | number | no | `3.5` | 1 – 10 | Styrke for classifier-free guidance; mulig kvalitetstap byttes mot tettere samsvar med prompten når skalaen økes. |
| `guidance_scale_2` |  | number | no | `4` | 1 – 10 | Veiledningsskala som styrer hvor tett modellen følger prompten i det andre trinnet. |
| `shift` |  | number | no | `5` | 1 – 10 | Videoforskyvningsinnstilling, med tillatt område på 1.0–10.0. |
| `interpolator_model` |  | enum (string) | no | `film` | `none`, `film`, `rife` | Innstilling for bildeinterpolering: velg None for å hoppe over interpolering, eller angi modellen som skal utføre den. |
| `num_interpolated_frames` |  | integer | no | `1` | 0 – 4 | Sett inn 0–4 interpolerte bilder i hvert mellomrom mellom to genererte bilder. |
| `adjust_fps_for_interpolation` |  | boolean | no | `true` |  | Bryter for justering av bildefrekvens: false bevarer den oppgitte FPS-en, mens true skalerer den med én pluss antallet interpolerte bilder. Dermed blir 16 FPS til 32 FPS når 1 bilde interpoleres og justering er aktivert. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Kvalitetsinnstilling for utdata-videoen som bytter større filer mot bedre visuelle resultater når verdien økes. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Modus for videoskriving, der balanced gir et kompromiss mellom hastighet og kvalitet. Velg small for de minste filene og tregeste skriving; raskere skriving returnerer resultater tidligere, men oppretter større filer. |
| `loras` |  | array of object {path, weight_name, scale, transformer} | no | `[]` |  | LoRA-vekter som brukes under inferens. |
| `loras[].path` |  | string | yes |  |  | Plasseringen til LoRA-vektene, angitt som en URL eller bane. |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA-vektnavn, bare obligatorisk når et Hugging Face-repositorium inneholder mer enn 1 safetensors-fil. Navnet brukes bare når `path` identifiserer et slikt repositorium. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Faktor brukt på LoRA-vekten før den flettes inn i basismodellen. |
| `loras[].transformer` |  | enum (string) | no | `high` | `high`, `low`, `both` | Transformer-mål for LoRA-vekten. Velg 'high' for transformeren med høy støy, 'low' for transformeren med lav støy, eller 'both' for å laste vekten inn i begge transformere. |
| `reverse_video` |  | boolean | no | `false` |  | Bryter for reversering av video; true reverserer avspillingen. |

## Example input

Required parameters only:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/wan/v2.2-a14b/text-to-video/lora?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/wan/v2.2-a14b/text-to-video/lora?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/wan/v2.2-a14b/text-to-video/lora", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.ru/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "wan/v2.2-a14b/text-to-video/lora",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "wan/v2.2-a14b/text-to-video/lora",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 507,
    "usd": "0.507",
    "charged": 507,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_name": "0-output.mp4",
      "file_size": 1843200
    },
    "prompt": "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere.",
    "seed": 42
  },
  "outputs": [
    {
      "type": "video",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_size": 1843200,
      "width": 1280,
      "height": 720,
      "duration": 5,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | Videofil produsert av genereringen. |
| `prompt` | string | Tekstprompt brukt til å generere videoen. |
| `seed` | integer | Seed-verdi brukt under genereringen. |

## Related models

- [Wan v2.2 A14B Text-to-Image A14B with LoRAs](https://doitong.com/developers/models/wan/v2.2-a14b/text-to-image/lora.md) (`wan/v2.2-a14b/text-to-image/lora`): $0.05 / image
- [Wan v2.2 A14B Image-to-Video A14B with LoRAs](https://doitong.com/developers/models/wan/v2.2-a14b/image-to-video/lora.md) (`wan/v2.2-a14b/image-to-video/lora`): from $1.006 / run
- [Wan 3.0 Text to Video](https://doitong.com/developers/models/alibaba/wan-3.0/text-to-video.md) (`alibaba/wan-3.0/text-to-video`): from $0.05 / second
- [FLUX 3 Max Text to Video](https://doitong.com/developers/models/blackforestlabs/flux-3-max/text-to-video.md) (`blackforestlabs/flux-3-max/text-to-video`): from $0.0425 / second of output video
- [Grok Imagine Video 1.5 Text to Video](https://doitong.com/developers/models/xai/grok-imagine-video/v1.5/text-to-video.md) (`xai/grok-imagine-video/v1.5/text-to-video`): from $0.08 / second
- [Kling 3.0 Pro Text to Video](https://doitong.com/developers/models/kling-video/v3/pro/text-to-video.md) (`kling-video/v3/pro/text-to-video`): from $0.112 / second
- [LTX 2.5 Pro Text to Video](https://doitong.com/developers/models/lightricks/ltx-2.5/text-to-video/pro.md) (`lightricks/ltx-2.5/text-to-video/pro`): from $0.12 / second
- [MiniMax H3 Text to Video](https://doitong.com/developers/models/minimax/h3/text-to-video.md) (`minimax/h3/text-to-video`): from $0.05 / second

## Links

- HTML page: https://doitong.com/developers/models/wan/v2.2-a14b/text-to-video/lora
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=wan/v2.2-a14b/text-to-video/lora
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/wan/v2.2-a14b/text-to-video/lora